Chen et al. [1]} formulated inertial odometry as a sequential learning problem with a key observation that 2D motion displacements in the polar coordinate (i.e. polar vector) can be learned from independent windows of segmented inertial data. The key observation is that when tracking human and wheeled configurations, the frequency of their vibrations is relevant to the moving speed, which is reflected by inertial measurements.
Based on this, they proposed IONet, a LSTM based framework for end-to-end learning of relative poses from sequences of inertial measurements. Trajectories are generated by integrating motion displacements.
[2]} leveraged deep generative models and domain adaptation technique to improve the generalization ability of deep inertial odometry in new domains. [3]} extends this framework by an improved triple-channel LSTM network to predict polar vectors for drone localization from inertial data and sampling time.
RIDI [4]} trains a deep neural network to regress linear velocities from inertial data, calibrates the collected accelerations to satisfy the constraints of the learned velocities, and doubly integrates the accelerations into locations with a conventional physical model.
Similarly, [5]} compensates the error drifts of the classical SINS model with the aid of learned velocities.
Other works have also explored the usage of deep learning to detect zero-velocity phase for navigating pedestrians [6]} and vehicles [7]}. This zero-velocity phase provides context information to correct system error drifts via Kalman filtering.
